Monthly Archives: February 2010

*Note: My original blog was posted on 05/08/2009. This is a copy of that blog.*

This study examines whether the Social Web, such as Social Search Engine (SSE) and/or Social Tagging (ST), improve productivity and efficiency among users in terms of finding web resources/information. It is hypothesized that SSE, as opposed to Traditional Search Engine (TSE), will enhance productivity due to it returning relevant search results as well as enhancing search experience.

The ever-expanding web resources and web information on the Internet is making it more and more difficult for people to find relevant information in a timely manner. The emerging search and categorization systems such as SSE and ST may be some excellent ways of excavating and facilitating information retrieval. The purpose of this study is to examine whether SSE and ST, as opposed to TSE, improve productivity, efficiency, satisfaction, and the emotional experiences of the users.

For the past two decades, the World Wide Web has witnessed and extremely rapid growth. Initially, the trend for finding information or web resources was to browse the Internet, also known as ‘surfing the net’. For this reason, sites such as Yahoo! (yahoo.com) gained more and more popularity because it packaged various web resources into one single portal. But with the rapid growth of the Internet, browsing the web appeared to be less effective. Gradually, this need led to the birth of search engines, such as Google search (google.com).

Now, the question is whether emerging people-categorization systems such as SSE and ST will facilitate and increase search results relevancy. The interesting aspect of these systems is that by using people-language the search results may improve. In other words, these systems use people’s natural language in order to categorize web resources. If it is people who search for web resources, then why not use people’s own knowledge to group and categorize these resources? Wouldn’t letting people properly categorize web resources in their own natural language return search results that are more relevant to their own search?